Key Takeaways

  • 75% of U.S. employers use automated applicant tracking systems to screen resumes before a human reviews them (Harvard Business School & Accenture, 2021)
  • The most common ATS failures are missing keywords, incompatible formatting, and incorrect file types
  • ResumeGeni scores your resume across 8 parsing layers — modeled on the same steps enterprise ATS platforms like Workday, Greenhouse, and Taleo use to evaluate candidates

How ATS Resume Scoring Works

Applicant tracking systems parse your resume into structured data — extracting your name, contact info, work history, skills, and education — then score how well that data matches the job requirements. Many ATS rejections happen because the parser couldn't extract critical fields, not because the candidate wasn't qualified.

LayerWhat It ChecksWhy It Matters
Document extractionFile format, encoding, readabilityCorrupted or image-only PDFs fail immediately
Layout analysisTables, columns, headers, footersMulti-column layouts break field extraction
Section detectionExperience, education, skills headingsNon-standard headings cause sections to be missed
Field mappingName, email, phone, dates, titlesMissing contact info is a common cause of immediate rejection
Keyword matchingJob-specific terms, skills, certificationsKeyword overlap affects recruiter search visibility and ATS scoring
Chronology checkDate ordering, gap detectionReverse-chronological order is expected by most ATS
QuantificationMetrics, numbers, measurable outcomesQuantified achievements help human reviewers and some scoring models
Confidence scoringOverall parse quality and completenessLow-confidence parses get deprioritized in results

Frequently Asked Questions

Is ResumeGeni free?
Yes. ResumeGeni is currently in beta — ATS analysis, scoring, and initial improvement suggestions are free with no signup required. Full guidance and saved reports may require a free account.
What file formats are supported?
PDF, DOCX, DOC, TXT, RTF, ODT, and Apple Pages. PDF and DOCX are recommended for best ATS compatibility.
How is the ATS score calculated?
Your resume is processed through an 8-layer parsing pipeline that extracts structured data the same way enterprise ATS platforms do. The score reflects how completely and accurately your resume can be parsed, plus how well your content matches common ATS ranking criteria.
Can ATS read PDF resumes?
Yes, but not all PDFs are equal. Text-based PDFs parse well. Image-only PDFs (scanned documents) and PDFs with complex tables or multi-column layouts often fail ATS parsing. Our analyzer will flag these issues.
How do I improve my ATS score?
Focus on three areas: use a clean single-column format, include keywords from the job description naturally in your experience bullets, and ensure all sections (contact, experience, education, skills) use standard headings.

ATS Guides & Resources

Built by engineers with 12 years of experience building enterprise hiring technology at ZipRecruiter. Last updated .

Analytics Engineering Advocate - Europe

Lightdash · Remote

About Lightdash

Lightdash is an open source BI tool that instantly turns your dbt project into a full-stack BI platform.

We’re building the best BI tool for analytics engineers by letting them manage everything as code, from the comfort of their text editor and command line. Once the data team has written the metrics, then Lightdash enables self-serve for the entire business, so your data is accessible for the whole team.

We’re helping data teams build data-driven companies so they can make better decisions, faster.

How we work at Lightdash

  • We build in public, by default. We’re an open source product, so having shared context is important so that people can contribute to Lightdash. As a team, we also think that we make the best decisions when everyone has a lot of the same information. We don't oversell and under-deliver: we want the experience of using Lightdash to be as awesome as the experience we're selling.

  • We challenge problems, not people. We ask ourselves “why is this broken?” not “who is breaking this?”

  • We’re highly collaborative. We’re a group of people that are happy working independently, but love being part of a team. ****We not only work on problems as a team, but we also listen to the feedback from our community and our users. We invest in tools and processes that allow us to do this, even while fully remote.

  • We bias towards impact. We’d rather build something to 80% and get it in front of users so we can iterate on fast feedback than build something to 100% just to find out it’s not the right thing. We spend a lot of time thinking about how our work solves real user problems. We work on the highest impact problems even if they’re something a bit “out of your remit”.

About the role

At Lightdash, we're obsessed with the success of our users. Our fast, empathetic, and deeply technical support is one of our superpowers. We help teams with everything from building dashboards and writing SQL to analytics engineering best practices and data modeling strategy. If you have a passion for helping teams win with data, and the technical skills to debug their toughest problems, we're looking for an Analytics Engineering Advocate to join the Lightdash team.

Our users' experience of Lightdash goes beyond product features. It includes every touchpoint and interaction they have with our community and team. In this role, you'll be the voice for our community, combining technical expertise with relationship-building to help users succeed with Lightdash and modern analytics engineering. You'll spend a significant portion of your time directly helping users in Slack and on calls — diagnosing issues, answering questions, and pair-programming through problems.

A note on AI: We're investing heavily in AI-powered workflows throughout Lightdash, and we expect this role to be at the forefront of that evolution. You'll use AI tools daily—for task management, technical problem-solving, analytics engineering work, and potentially contributing to our codebase. The ideal candidate is excited about the rapid pace of AI development and eager to incorporate new capabilities into their work and share that knowledge with our community.

Areas of Responsibility

Obsess over user success through technical problem-solving

Our users' wins are our wins. You'll be active daily in shared customer Slack channels, responding to user questions, triaging bugs, and jumping on calls to pair-program through complex issues. You'll balance fast, practical solutions with thoughtful, strategic guidance on analytics architecture and process improvement. You'll leverage AI tools to work more efficiently and help users understand how to do the same.

Be the Lightdash expert

You'll be the go-to Lightdash pro, both internally and in the community. You'll stay current with our latest features, including our evolving AI capabilities (using Lightdash for our own analytics and demos), understand how they fit into broader BI and analytics engineering workflows, and share this knowledge widely. You'll represent Lightdash at community events, conferences, and meetups with curiosity and enthusiasm, showcasing how AI is transforming analytics workflows.

Teach and share

Create and deliver world-class tutorials and demos for Lightdash and examples of analytics engineering best practices. Whether through docs, guides, blog posts, video tutorials, or code examples, you'll make complex concepts accessible and help users level up their skills. You'll use AI tools to accelerate content creation while maintaining quality and authenticity.

Build and grow community

Develop and execute strategies to grow and engage the analytics engineering community around Lightdash. This includes cultivating relationships with community members, identifying product champions, and creating spaces for knowledge sharing and collaboration around modern analytics practices.

What we're looking for

Technical expertise

  • Strong hands-on experience with dbt, SQL, data modeling, and modern data stacks

  • Comfortable with git, the command line, and data visualization tools

  • Proficient with AI coding assistants and productivity tools; you should be comfortable using AI to write code, debug issues, and accelerate your work

  • Resilience when facing ambiguous or complex debugging challenges

Industry knowledge and product vision

  • Deep understanding of business intelligence and analytics engineering workflows

  • Ability to connect user needs to product strategy and feature development

  • Excitement about how AI is transforming the analytics landscape and eagerness to stay current as the space evolves rapidly

Communication and relationship building

  • Exceptional written and oral communication skills—you can explain technical concepts clearly to varied audiences

  • Natural teacher who can train people through live calls, in-person, videos, documentation, etc.

  • Strong empathy, curiosity, and active listening skills; you genuinely care about understanding users' contexts and constraints

  • Relationship-builder who enjoys connecting with community members

Logistics

  • Able to work UTC +/- 3

💜 We believe that to build a product that works for a diverse group of people, we need a diverse team. So, we strongly encourage candidates of all different perspectives, experiences, backgrounds and identities to apply. We’re committed to hiring people regardless of race, religion, colour, national origin, sex, sexual orientation, gender identity, age or disability. And once you join us, we’re committed to building an inclusive, supportive place for you to do the best work of your career.